class_docstr: Each `Instance` in a `Scenario` looks like this:
  
      <input> -> <reference1>
                 <reference2>
                 <reference3> [correct]
                 <reference4>
  
  We can define a label (e.g., letter) for each reference:
  
      <instructions>
  
      <input>                  # train
      A. <reference1>
      B. <reference2>
      C. <reference3>
      D. <reference4>
      Answer: C
  
      <input>                  # test
      A. <reference1>
      B. <reference2>
      C. <reference3>
      D. <reference4>
      Answer:
  
  In general, each example is:
  
      <input_prefix><input><reference_prefixes[0]><reference><output_prefix><output>
code: def generate_requests(
      self, eval_instance: Instance
  ) -> List[RequestState]:
      prompt = self.construct_prompt(
          self.train_instances,
          eval_instance,
          include_output=False,
          reference_index=None,
      )
      output_mapping: Dict[str, str] = dict(
          (
              self.get_reference_prefix("A", reference_index),
              reference.output.text,
          )
          for reference_index, reference in enumerate(
              eval_instance.references
          )
      )
      request = Request(
          model=self.adapter_spec.model,
          prompt=prompt.text,
          num_completions=1,
          top_k_per_token=self.adapter_spec.num_outputs,
          temperature=self.adapter_spec.temperature,  # usually this is 0
          max_tokens=self.adapter_spec.max_tokens,  # usually this is 1
          stop_sequences=[],
          random=self.adapter_spec.random,
      )
      request_state = RequestState(
          instance=eval_instance,
          reference_index=None,
          request_mode=None,
          train_trial_index=self.train_trial_index,
          output_mapping=output_mapping,
          request=request,
          result=None,
          num_train_instances=prompt.num_train_instances,
          prompt_truncated=prompt.truncated,
      )
      return [request_state]
  
file_path: H2O/h2o_hf/helm/src/helm/benchmark/adaptation/adapters/multiple_choice_joint_adapter.py
imports: from typing import List, Dict, Optional
  from helm.benchmark.adaptation.request_state import RequestState
  from helm.benchmark.scenarios.scenario import Instance
  from helm.common.request import Request
  from .in_context_learning_adapter import InContextLearningAdapter
label_desc: Use image node_id 2 for calling the MultipleChoiceJointAdapter obj's underlying member method code with example usage: obj.generate_requests(eval_instance) and returns: list